---
title: "Dashboard Felicidad y Salud en Europa"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed # Añade un enlace al código fuente si lo deseas

---


```{r setup, include=FALSE} 

### SETUP:

# Definir el repositorio CRAN para instalar paquetes 
options(repos = c(CRAN = "<https://cloud.r-project.org>"))

# Paquetes necesarios

packages <- c("knitr","flexdashboard", "ggplot2", "dplyr", "ggridges", "viridis", "plotly", "readr", "here", "shiny", "sf", "rnaturalearth")

# Instalar automáticamente si faltan

installed <- packages %in% rownames(installed.packages()) 
  if (any(!installed)) { install.packages(packages[!installed]) }

# Cargar librerías y dataset

lapply(packages, library, character.only = TRUE)
df <- read.csv(here("Data", "ESS11-depurado.csv"))
```

Salud x Felicidad
=======================================================================
Column {data-width=150}
-----------------------------------------------------------------------

### Felicidad por país

```{r fig.height=7, fig.width=7, out.height="100%"}
sf::sf_use_s2(FALSE)
cntry_counts <- df %>%
  group_by(cntry) %>%
  summarise(n_part = n())

# Descargar mapa mundial
world <- ne_countries(returnclass = "sf") %>%
  st_make_valid()  # Arregla posibles geometrías inválidas

# 3 Unir datos de participantes
map_data <- world %>%
  left_join(cntry_counts, by = c("iso_a2_eh" = "cntry"))

#  Calcular puntos dentro de cada país para etiquetas
points <- st_point_on_surface(map_data)

# Definir recorte: de Islandia a Israel
bbox <- st_bbox(c(xmin = -25, xmax = 40, ymin = 28, ymax = 85))
map_data_crop <- st_crop(map_data, bbox)
points_crop <- st_crop(points, bbox)


df_happy <- df %>%
  group_by(cntry) %>%
  summarise(mean_happy = mean(happy, na.rm = TRUE),
            n = n())

world_happy <- map_data_crop %>%
  left_join(df_happy, by = c("iso_a2_eh" = "cntry"))

ggplot(world_happy) +
  geom_sf(aes(fill = mean_happy)) +
  scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
  labs(fill = "Felicidad media", title = "Felicidad media por país") +
  coord_sf(expand = FALSE) + 
  theme_minimal() + 
  theme (plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"))

```

### Salud por país

```{r, fig.height=7, fig.width=7, out.height="100%"}

df_health <- df %>%
  group_by(cntry) %>%
  summarise(mean_health = mean(health, na.rm = TRUE),
            n = n())
world_health <- map_data_crop %>%
  left_join(df_health, by = c("iso_a2_eh" = "cntry"))

ggplot(world_health) +
  geom_sf(aes(fill = mean_health)) +
  scale_fill_viridis_c(option = "plasma", na.value = "grey90") +
  labs(fill = "Salud media") +
  coord_sf(expand = FALSE) +
  theme_minimal() + 
  theme (plot.margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm"))
```

Column {data-width=200}
-----------------------------------------------------------------------
### Relación salud y felicidad: número de respuestas

```{r, fig.height=8, fig.width=6, out.width="100%", out.height="auto"}

# Tabla de proporciones
df_bubble <- df %>%
  count(health, happy) %>%
  group_by(health) %>%
  mutate(prop = n / sum(n))

# Bubble heatmap con línea de tendencia
ggplot(df_bubble, aes(x = as.numeric(health), y = as.numeric(happy))) +
  geom_point(aes(size = n, color = prop), alpha = 0.7) +
  geom_smooth(aes(weight = n), method = "loess", color = "red", se = FALSE) +
  scale_size(range = c(3, 15)) +
  scale_color_gradient(low = "lightblue", high = "darkblue") +
  labs(
    x = "Salud autopercibida",
    y = "Felicidad autopercibida",
    size = "N",
    color = "Prop",
    title = "Salud vs Felicidad (proporciones)"
  ) +
  theme_minimal() +
  theme(
   legend.position = "bottom",
   legend.box = "horizontal",
   legend.box.just = "center",
    legend.spacing.x = unit(1, "cm"),
    legend.text = element_text(size = 6)
  ) +
  guides(
    size = guide_legend(nrow = 2, byrow = TRUE),
    color = guide_colorbar(barwidth = 5, barheight = 1)
  )
```

Column {data-width=200}
-----------------------------------------------------------------------
### Relación salud y felicidad: proporciones de respuesta

```{r, fig.height=8, fig.width=6, out.width="100%", out.height="auto"}

# Calcular proporciones por combinación happy x health
df_heatmap <- df %>%
  filter(!is.na(happy) & !is.na(health)) %>%
  count(health, happy) %>%
  group_by(health) %>%
  mutate(prop = n / sum(n))

# Calcular tendencia: media de happy por cada nivel de health
trend <- df %>%
  filter(!is.na(happy) & !is.na(health)) %>%
  group_by(health) %>%
  summarise(mean_happy = mean(happy, na.rm = TRUE))

# Heatmap con tendencia
ggplot(df_heatmap, aes(x = as.numeric(health), y = as.numeric(happy), fill = prop)) +
  geom_tile(color = "white", alpha = 0.9) +
  scale_fill_gradient(low = "lightblue", high = "darkblue") +
  geom_text(aes(label = scales::percent(prop, accuracy = 1)), color = "white", size = 3) +
  geom_line(
    data = trend,
    aes(x = as.numeric(health), y = mean_happy),
    color = "red", size = 1,
    inherit.aes = FALSE
  ) +
  geom_point(
    data = trend,
    aes(x = as.numeric(health), y = mean_happy),
    color = "red", size = 2,
    inherit.aes = FALSE
  ) +
  labs(
    x = "Salud autopercibida",
    y = "Felicidad autopercibida",
    fill = "Proporción",
    title = "Mapa de calor: Salud vs Felicidad"
  ) +
  scale_x_continuous(breaks = unique(as.numeric(df$health))) +
  scale_y_continuous(breaks = unique(as.numeric(df$happy))) +
  theme_minimal() +
  theme(legend.position = "bottom")

```



Felicidad en función de otros factores sociodemográficos
=======================================================================

Column {data-width=200}
-----------------------------------------------------------------------
### Felicidad por rangos de edad

```{r, fig.height=6, fig.width=8, out.width="auto", out.height="100%"}
# Filtrar NA en happy y crear rangos de edad
df_ridges <- df %>%
  filter(!is.na(happy) & !is.na(agea)) %>%
  mutate(age_group = cut(agea, breaks = seq(15, 90, by = 5), include.lowest = TRUE))

# Calcular media de felicidad por grupo de edad para la línea de tendencia
trend1 <- df_ridges %>%
  group_by(age_group) %>%
  summarise(mean_happy = mean(happy),
            mid_age = mean(as.numeric(age_group)))  # posición central para la línea

# Ridge plot con línea de tendencia
ggplot(df_ridges, aes(x = happy, y = age_group, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
  geom_point(
    data = trend1,
    aes(x = mean_happy, y = age_group, color = "Felicidad media"), size = 2) +
  geom_smooth(
    data = trend1,
    aes(x = mean_happy, y = as.numeric(age_group), color = "Felicidad media"),
    method = "loess",
    se = FALSE,
    linewidth = 1) +
  scale_fill_viridis_c(option = "plasma", guide = guide_colorbar(direction = "horizontal"), alpha = 0.9) +
  scale_x_continuous(breaks = 0:10, limits = c(0, 11)) +
  scale_color_manual(values = c("Felicidad media" = "red")) +   
  labs(
    x = "Felicidad",
    y = "Rango de edad",
    fill = "Felicidad",
    color = "") +
  theme_minimal() +
  ggtitle("Distribución de felicidad por rangos de edad") +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_text(size = 12),
    axis.text.x = element_text(size = 10),
    axis.title.x = element_text(size = 12),
    legend.text = element_text(size = 8),
    legend.position = "bottom",     # 📍 leyenda a la derecha
    legend.box = "horizontal"        # apila leyendas verticalmente
  )


```

Column {data-width=200}
-----------------------------------------------------------------------

### género x felicidad

```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop3 <- df %>%
  filter(!is.na(happy)& !is.na(gndr)) %>%
  group_by(gndr, happy) %>%
  summarise(n = n()) %>%
  mutate(prop = n / sum(n))

ggplot(df_prop3, aes(x = factor(gndr), y = prop, fill = factor(happy))) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_viridis_d(option = "plasma") +
  scale_x_discrete(labels = c("1" = "Hombre", "2" = "Mujer")) + 
  labs(x = "Género", y = "Proporción", fill = "Nivel de felicidad percibida",
       title = "Distribución de felicidad por género") +
  theme_minimal()

```

### Felicidad x país de nacimiento

```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop1 <- df %>%
  filter(!is.na(happy)& !is.na(brncntr)) %>%
  group_by(brncntr, happy) %>%
  summarise(n = n()) %>%
  mutate(prop = n / sum(n))


ggplot(df_prop1, aes(x = factor(brncntr), y = prop, fill = factor(happy))) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_viridis_d(option = "plasma", alpha = 0.9) + 
  labs(x = "Origen", y = "Proporción", fill = "Nivel de felicidad percibida",
       title = "Distribución de felicidad por origen") +
  theme_minimal()

```


Salud percibida en función de otros factores sociodemográficos
=======================================================================
Column {data-width=200}
-----------------------------------------------------------------------

### Salud x edad
```{r, fig.height=6, fig.width=8, out.width="auto", out.height="100%"}

# Filtrar NA en health y crear rangos de edad
df_ridges2 <- df %>%
  filter(!is.na(health) & !is.na(agea)) %>%
  mutate(age_group = cut(agea, breaks = seq(15, 90, by = 5), include.lowest = TRUE))

# Calcular media de health por grupo de edad para la línea de tendencia
trend2 <- df_ridges2 %>%
  group_by(age_group) %>%
  summarise(mean_health = mean(health),
            mid_age = mean(as.numeric(age_group)))  # posición central para la línea


ggplot(df_ridges2, aes(x = health, y = age_group, fill = stat(x))) +
  geom_density_ridges_gradient(scale = 2, rel_min_height = 0.00001) +
  geom_point(
    data = trend2,
    aes(x = mean_health, y = age_group, color = "Salud media"), size = 2) +
  geom_smooth(
    data = trend2,
    aes(x = mean_health, y = as.numeric(age_group), color = "Salud media"),
    method = "loess",
    se = FALSE,
    linewidth = 1) +
  scale_fill_viridis_c(option = "plasma", guide = guide_colorbar(direction = "horizontal")) +
  scale_x_continuous(breaks = 0:5, limits = c(0, 5.5)) +
  scale_color_manual(values = c("Salud media" = "red")) +   
  labs(
    x = "Salud",
    y = "Rango de edad",
    fill = "Salud",
    color = "") +
  theme_minimal() +
  ggtitle("Distribución de salud por rangos de edad") +
  theme(
    axis.text.y = element_text(size = 10),
    axis.title.y = element_text(size = 12),
    axis.text.x = element_text(size = 10),
    axis.title.x = element_text(size = 12),
    legend.text = element_text(size = 8),
    legend.position = "bottom",     # 📍 leyenda a la derecha
    legend.box = "horizontal"        # apila leyendas verticalmente
  )
```


Column {data-width=200}
-----------------------------------------------------------------------
### Genero x salud

```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}

df_prop4 <- df %>%
  filter(!is.na(gndr)& !is.na(health)) %>%
  group_by(gndr, health) %>%
  summarise(n = n()) %>%
  mutate(prop = n / sum(n))

ggplot(df_prop4, aes(x = factor(gndr), y = prop, fill = factor(health))) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_viridis_d(option = "plasma") +
  scale_x_discrete(labels = c("1" = "Hombre", "2" = "Mujer")) + 
  labs(x = "Género", y = "Proporción", fill = "Nivel de salud percibida",
       title = "Distribución de salud por género") +
  theme_minimal()
```


### Salud x país nacimiento
```{r, fig.height=6, fig.width=8, out.width="100%", out.height="auto"}
df_prop2 <- df %>%
  filter(!is.na(brncntr)& !is.na(health)) %>%
  group_by(brncntr, health) %>%
  summarise(n = n()) %>%
  mutate(prop = n / sum(n))

ggplot(df_prop2, aes(x = factor(brncntr), y = prop, fill = factor(health))) +
  geom_bar(stat = "identity", position = "fill") +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_viridis_d(option = "plasma") +
  labs(x = "Origen", y = "Proporción", fill = "Nivel de salud percibida",
       title = "Distribución de salud por origen") +
  theme_minimal()
```